Book Image

Hands-On Data Structures and Algorithms with JavaScript

By : Kashyap Mukkamala
Book Image

Hands-On Data Structures and Algorithms with JavaScript

By: Kashyap Mukkamala

Overview of this book

Data structures and algorithms are the fundamental building blocks of computer programming. They are critical to any problem, provide a complete solution, and act like reusable code. Using appropriate data structures and having a good understanding of algorithm analysis are key in JavaScript to solving crises and ensuring your application is less prone to errors. Do you want to build applications that are high-performing and fast? Are you looking for complete solutions to implement complex data structures and algorithms in a practical way? If either of these questions rings a bell, then this book is for you! You'll start by building stacks and understanding performance and memory implications. You will learn how to pick the right type of queue for the application. You will then use sets, maps, trees, and graphs to simplify complex applications. You will learn to implement different types of sorting algorithm before gradually calculating and analyzing space and time complexity. Finally, you'll increase the performance of your application using micro optimizations and memory management. By the end of the book you will have gained the skills and expertise necessary to create and employ various data structures in a way that is demanded by your project or use case.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
PacktPub.com
Contributors
Preface
5
Simplify Complex Applications Using Graphs
Index

Terminology


The terminology used when discussing the space and time complexity of an algorithm is something that one, as a developer, will come across very often. Popular terms such as Big-Oalso known as O (something)and some not-so-popular terms such as Omega (something) or Theta (something) are often used to describe the complexity of an algorithm. The O actually stands for Order, which represents the order of the function.

Let's first talk only about the time complexity of an algorithm. This basically boils down to us trying to figure out how long it will take for a system to execute our algorithm for a given dataset (D). We can technically run this algorithm on the said system and log its performance, but since not all systems are the same (for example, OS, number of processors, and read write speeds), we can't necessarily expect the result to truly represent the average time it would take to execute our algorithm for our dataset, D. At the same time, we would also need to know how...